Inference Optimization

The Inference Optimization module provides tools for reducing model size, improving inference latency, and maximizing throughput. Techniques include quantization, pruning, knowledge distillation, ONNX conversion, and TensorRT compilation. The implementation is in src/inference/inference_optimizer.py.

Optimization Techniques

Technique	Latency Reduction	Size Reduction	Accuracy Impact
ONNX Conversion	20-40%	Minimal	None
INT8 Quantization	40-60%	50-75%	Low (less than 1%)
FP16 Mixed Precision	30-50%	50%	Minimal
Pruning	20-50%	30-70%	Low to moderate
Knowledge Distillation	Variable	50-90%	Moderate
TensorRT Compilation	50-70%	Variable	None to low
Dynamic Batching	N/A (throughput)	N/A	None

Optimize Model

POST /api/v1/inference/optimize

{
  "model_id": "model-xyz789",
  "optimizations": ["onnx_conversion", "quantization"],
  "quantization_config": {
    "method": "dynamic",
    "precision": "int8"
  },
  "validation_dataset": {
    "source": "sql",
    "query": "SELECT * FROM ml_features.customer_churn LIMIT 1000"
  },
  "accuracy_threshold": 0.99
}

Response

{
  "optimization_id": "opt-abc123",
  "status": "completed",
  "original": {
    "format": "pkl",
    "size_mb": 245,
    "latency_ms": 15.2,
    "accuracy": 0.912
  },
  "optimized": {
    "format": "onnx",
    "size_mb": 62,
    "latency_ms": 6.8,
    "accuracy": 0.910
  },
  "improvement": {
    "size_reduction": "74.7%",
    "latency_reduction": "55.3%",
    "accuracy_change": "-0.002"
  },
  "artifact_id": "model-xyz789-optimized"
}

ONNX Conversion

Converts models from framework-specific formats to ONNX for portable, optimized inference:

class InferenceOptimizer:
    def convert_to_onnx(self, model, sample_input):
        import onnx
        import onnxruntime as ort
 
        onnx_path = self._export_onnx(model, sample_input)
        onnx_model = onnx.load(onnx_path)
        onnx.checker.check_model(onnx_model)
        return onnx_path

Source Framework	Conversion Method
scikit-learn	`skl2onnx` converter
XGBoost	`onnxmltools` converter
LightGBM	`onnxmltools` converter
PyTorch	`torch.onnx.export`
TensorFlow	`tf2onnx` converter

Quantization

Reduces numerical precision of model weights and activations:

Quantization Type	Description	Supported
Dynamic	Quantize weights statically, activations dynamically	All ONNX models
Static	Calibrate with representative data	ONNX with calibration set
Quantization-Aware Training	Train with simulated quantization	PyTorch models

Pruning

Removes low-importance weights or neurons to reduce model size:

Pruning Method	Description
Magnitude	Remove weights below threshold
Structured	Remove entire filters or attention heads
Lottery Ticket	Find sparse subnetwork

Benchmarking

After optimization, the module runs benchmarks to validate performance:

{
  "benchmark": {
    "iterations": 1000,
    "warmup_iterations": 100,
    "batch_sizes": [1, 16, 32, 64],
    "results": {
      "batch_1": {"p50_ms": 5.2, "p95_ms": 8.1, "p99_ms": 12.3},
      "batch_16": {"p50_ms": 8.5, "p95_ms": 14.2, "p99_ms": 21.0},
      "batch_32": {"p50_ms": 12.1, "p95_ms": 19.8, "p99_ms": 28.5}
    }
  }
}

Configuration

Environment Variable	Default	Description
`OPTIMIZATION_MAX_TIME_MINUTES`	`30`	Max optimization time
`OPTIMIZATION_ACCURACY_THRESHOLD`	`0.99`	Min accuracy ratio vs. original
`TENSORRT_WORKSPACE_SIZE_GB`	`4`	TensorRT workspace memory
`ONNX_OPSET_VERSION`	`17`	ONNX opset version

Shadow Deployment Monitoring & Drift